Lead Generation Tools and Data Sources in PropTech – With example list

Accounts rather than contacts: Commercial opportunities in PropTech are usually tied to assets, portfolios, or development programmes rather than single individuals. An account might represent a property owner, an operator, a developer, or an advisory firm, with multiple stakeholders involved over time. Thinking in this way helps teams focus on ownership structures, influence, and decision‑making context rather than simply collecting names. 

Data quality as a prerequisite: Regardless of which tools are used, data quality underpins effective lead generation. Incomplete or outdated information undermines reporting, forecasting, and prioritisation. This is a persistent challenge in property markets, where organisational structures and job titles change frequently. 

The importance of context in property markets: Property and built‑environment markets are place‑based and asset‑specific. Lead generation therefore depends on understanding geography, planning status, building characteristics, and ownership structures. Without this context, data can easily be misread or misinterpreted. 

Responsible data use at a high level: Across the UK and EU, lead generation operates within a strict framework of data protection and accountability. Even when data is publicly accessible, organisations remain responsible for using it ethically and appropriately. This resource highlights these issues at a conceptual level rather than providing legal advice. 

What this category supports: CRM systems act as the central record for accounts, contacts, and deal pipelines. They bring together sales, marketing, and customer activity in one place and provide shared visibility across teams. 

Why it matters in PropTech: With long sales cycles and multiple stakeholders, continuity matters. CRMs help preserve the institutional memory of an opportunity as it progresses over months or years, even as individuals change roles or organisations. 

Common limitations or trade‑offs: A CRM is only as effective as the data entered into it. Poor data quality can quickly undermine productivity and lead to poor decision‑making. 

High‑level data‑use and regulatory considerations: CRMs typically contain a mix of personal and business data. Responsibility for lawful and appropriate use always sits with the organisation, not the software provider. 

Examples of commonly used tools: Salesforce · HubSpot CRM · Microsoft Dynamics 365 · Pipedrive · Zoho CRM 

What this category supports: These platforms support lead capture, segmentation, and lifecycle tracking. They link inbound activity such as event attendance or content downloads to contact records and help teams understand engagement over time. 

Why it matters in PropTech: Many PropTech solutions are education‑led, particularly in areas such as ESG, analytics, or compliance. Lifecycle platforms help teams track how prospects interact with educational content as part of a longer buying journey. 

Common limitations or trade‑offs: There is often functional overlap between marketing automation platforms, CRMs, and sales tools. Without clear internal boundaries, organisations may end up duplicating features across systems. 

High‑level data‑use and regulatory considerations: These platforms track personal interactions and engagement data. Use must align with data‑protection requirements and the specific terms of each platform. 

Examples of commonly used tools: HubSpot Marketing Hub · Adobe Marketo · Salesforce Account Engagement (Pardot) · ActiveCampaign · Mailchimp 

What this category supports: Sales‑intelligence platforms help teams find and enrich company and contact data. They often provide firmographic information, role data, and signals based on online activity. 

Why it matters in PropTech: The property ecosystem involves developers, managing agents, advisers, and operators. Data enrichment helps teams map these relationships and identify where decision‑making power is likely to sit. 

Common limitations or trade‑offs: Data decay and inconsistent sources are persistent challenges. Information can become outdated quickly, particularly in sectors with frequent role changes. 

High‑level data‑use and regulatory considerations: Access to contact data does not remove obligations around transparency, lawful use, and respect for individual rights. 

Examples of commonly used tools: LinkedIn Sales Navigator · ZoomInfo · Cognism · Apollo · Lusha 

What this category supports: Sales‑engagement platforms help teams plan, manage, and analyse interactions across multiple activities and channels. 

Why it matters in PropTech: PropTech deals typically involve many touchpoints over extended periods. These platforms help teams see patterns of engagement across accounts rather than focusing on one‑off actions. 

Common limitations or trade‑offs: Effectiveness depends on data quality and clearly defined internal processes. Tools cannot compensate for unclear workflows. 

High‑level data‑use and regulatory considerations: Engagement tracking often involves personal data and interaction histories. Responsibility for appropriate use rests with the organisation. 

Examples of commonly used tools: Outreach · Salesloft · HubSpot Sales Hub · Groove · Yesware 

What this category supports: Professional networks help identify stakeholders through profiles, career histories, and shared connections. 

Why it matters in PropTech: These networks are often the most practical way to find asset managers, development leads, facilities heads, ESG specialists, and advisers. 

Common limitations or trade‑offs: Role data is self‑reported and may be outdated. Use of this information is governed by platform rules. 

High‑level data‑use and regulatory considerations: Users must comply with both platform policies and data‑protection obligations. 

Examples of commonly used sources: LinkedIn · LinkedIn Groups · Alumni networks · Industry directories · Professional associations 

What this category supports: Company registries provide official information on legal entities, directors, ownership, and corporate relationships. 

Why it matters in PropTech: Property ownership is often layered through holding companies and SPVs. Registry data helps validate who sits behind an asset or portfolio. 

Common limitations or trade‑offs: Registry data reflects legal status rather than commercial intent. Without additional context, it can be easy to misinterpret. 

High‑level data‑use and regulatory considerations: Public availability does not remove the need for responsible interpretation and handling. 

Examples of commonly used sources: Companies House · OpenCorporates · Moody’s Orbis · nCino (formerly DueDil) · Creditsafe 

What this category supports: These datasets provide asset‑level and place‑based context to support market analysis and segmentation. 

Why it matters in PropTech: Most PropTech solutions relate directly to buildings, land, or infrastructure. Asset‑level data helps ground sales activity in real‑world conditions. 

Common limitations or trade‑offs: Data should be used within its original scope. Over‑interpretation beyond intended use is a common risk. 

High‑level data‑use and regulatory considerations: Datasets often come with specific licences and usage constraints that must be respected. 

Examples of commonly used sources: HM Land Registry · Planning Data (England) · EPC Open Data · Ordnance Survey Open Data · Local authority open data portals 

What this category supports: Intent data aims to indicate research activity or interest at an account level using provider‑defined methodologies. 

Why it matters in PropTech: These signals can offer directional insight into topics an organisation may be exploring. 

Common limitations or trade‑offs: Signals vary widely depending on source and methodology and should not be treated as certainty. 

High‑level data‑use and regulatory considerations Extra care is required where inferred behaviour may link back to individuals. 

Examples of commonly used tools: Bombora · G2 Buyer Intent · Demandbase · 6sense · TechTarget Priority Engine 

What this category supports: This category includes conferences, memberships, accelerators, partner ecosystems, and industry networks. 

Why it matters in PropTech: The property sector remains reputation‑driven and relationship‑led. Network‑based discovery often complements data‑driven approaches. 

Common limitations or trade‑offs: These sources can be difficult to scale and may reflect existing market structures. 

High‑level data‑use and regulatory considerations: Data sharing within networks should be handled transparently and responsibly. 

Harvard Business Review — Bad Data Is Sapping Your Team’s Productivity 
https://hbr.org/2022/11/bad-data-is-sapping-your-teams-productivity 

Gartner — Revenue Operations 
https://www.gartner.com/en/sales/topics/revenue-operations 

Gartner — Sales Engagement: The Definitive Guide 
https://www.gartner.com/en/articles/sales-engagement-the-definitive-guide-for-platform-selection 

Gartner — Data Quality (topic overview) 
https://www.gartner.com/en/data-analytics/topics/data-quality 

Microsoft Learn — LinkedIn Sales Navigator documentation 
https://learn.microsoft.com/en-us/linkedin/sales/ 

Microsoft Learn — Sales Navigator compliance overview 
https://learn.microsoft.com/en-us/linkedin/compliance/compliance-sales-nav/overview 

Companies House API — Overview 
https://developer.company-information.service.gov.uk/overview/ 

HM Land Registry — Price Paid Data 
https://www.gov.uk/government/collections/price-paid-data 

HM Land Registry — INSPIRE Index Polygons 
https://use-land-property-data.service.gov.uk/datasets/inspire 

EPC Open Data Communities — Energy Performance Certificates 
https://epc.opendatacommunities.org/ 

Planning Data (England) 
https://www.planning.data.gov.uk/ 

Ordnance Survey — OS OpenMap – Local 
https://docs.os.uk/os-downloads/products/maps-and-imagery-portfolio/os-openmap-local 

Bombora — What is intent data? 
https://bombora.com/what-is-intent-data/ 

G2 — Buyer Intent documentation 
https://documentation.g2.com/docs/buyer-intent